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Change detection between multi-band images with different spatial and spectral resolutions

Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through different kinds of sensors. More precisely, this paper addresses the problem of detecting changes between two multi-band optical images characterized by different spatial and spectral resolutions. This sensor dissimilarity introduces additional issues in the context of operational change detection. To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e.g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images. Nevertheless, these preprocessing steps tend to throw away relevant information.
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Fig. 1. Change detection for high-resolution multispectral (HR-MS) and low-resolution hyperspectral (LR-HS) images: (a) HR-MS observed image, (b) LR-HS observed image, (c) actual change mask change, change mask estimated by the proposed robust fusion-based approach.

Change detection as a 3-step procedure

The proposed strategy consists of a 3-step procedure: i) inferring a high spatial and spectral resolution image by fusion of the two observed images characterized one by a low spatial resolution and the other by a low spectral resolution, ii) predicting two images with respectively the same spatial and spectral resolutions as the observed images by degradation of the fused one and iii) implementing a decision rule to each pair of observed and predicted images characterized by the same spatial and spectral resolutions to identify changes. The performance of the proposed framework is evaluated on real images with simulated realistic changes.

The proposed fusion-based change detection approach is detailed in the following submitted paper:

Change detection as a robust fusion problem

We propose a method that more effectively uses the available information by modeling the two observed images as spatial and spectral versions of two (unobserved) latent images characterized by the same high spatial and high spectral resolutions. As they cover the same scene, these latent images are expected to be globally similar except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a robust multi-band image fusion method which enforces the differences between the estimated latent images to be spatially sparse. This robust fusion problem is formulated as an inverse problem which is iteratively solved using an efficient block-coordinate descent algorithm. The proposed method is applied to real panchormatic/multispectral and hyperspectral images with simulated realistic changes. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy.

The proposed robust fusion-based change detection approach is detailed in the following submitted paper:

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